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Evaluation of Multiple Apache Spark Applications using Kubernetes as a Cluster manager on Google Cloud

Procedia computer science 2025年 252卷 576-582页

作者： M. Jayanthi K. Ram Mohan Rao Vuppala Sukanya Assistant Professor Dept of Computer Science and Informatics Mahatma Gandhi University Nalgonda Professor Dept of Information Technology Vasavi College of Engineering Hyderabad Assistant Professor Dept of CSE OU Hyderabad

Big data processing frameworks demands for scalable and efficient cluster management. Apache Spark has emerged as prominent big data processing framework providing high-speed data processing and analytics capabilities for multiple applications. This paper explores the integration of Kubernetes as a cluster manager for Apache Spark applications leveraging its containerization capabilities to improve resource utilization and simplify deployment. In this paper the challenges of deploying spark applications on traditional cluster managers and showcase the advantages of adopting Kubernetes are analysed. The experimental evaluation demonstrates the benefits of Kubernetes as a cluster manager for Apache Spark framework. To execute the multiple Apache Spark applications on Kubernetes a homogenous cluster on Google Cloud is created by History bucket and service account. Finally multiple applications are executed on Google Kubernetes Engine. Output can be shown as the number of executor pods created with the performance metrics can be viewed. In conclusion, this paper compares the performance metrics such as job execution time and resource utilization with the different cluster.

关键词： Homogenous Apache Spark Kubernetes Google Kubernetes Engine Containerization

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Enhancing Educational Video Discovery Using Advanced Latent Semantic Analysis

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Procedia computer science 2025年 252卷 784-795页

作者： B. Sindhu A. Bhaskar G. Yugesh S. Reshma B. Rohit Assistant Professor Department of Computer Science and Engineering Godavari Global University Rajamahendravaram -533296 Andhra Pradesh India

The rapid growth of online educational videos has created abundant learning resources, but also has challenges in discovering high-quality, relevant content. This project mainly focuses on video-based content analysis and providing high-quality learning sources for scholars, easing the best possible way to discover accurate resources. Existing Systems often rely on round-robin, random selection which fails to grasp semantics and linguistic complexity. The proposed system focuses on content-based analytics using NLP, where the extraction of transcripts from videos, perform relevance assessment which maps content to concept and extract insights using Latent Semantic Analysis which effectively captures underlying structures in the transcript. Complexity Assessment measures readability metrics using the Flesch-Kincaid Grade Level and SMOG Index. Created a web-based interface for easy access to educational videos, with built-in analytics to assess relevance. The insights are shared with scholars to refine content and better meet user requirements.

关键词： Latent Semantic Analysis NLP(Natural Language Processing) SMOG(Simple Measure of Gobbledygook) Index Topic Modelling TF-IDF (Term frequency-inverse document frequency) Content-based filtering

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GS-FFO Based Meta-Heuristic Clustering for Discovering Colossal Patterns in High-Dimensional Data 2nd

GS-FFO Based Meta-Heuristic Clustering for Discovering Colos...

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2nd Congress on Control, Robotics, and Mechatronics, CRM 2024

作者： Reddy, T. Sreenivasula Sathya, R. Nuka, Mallikharjuna Rao Research Scholar Department of Computer Science and Engineering Faculty of Engineering and Technology Annamalai University Annamalai Nagar Tamil Nadu Chidambaram608002 India Assistant Professor Department of Computer Science and Engineering Faculty of Engineering and Technology Annamalai University Annamalai Nagar Tamil Nadu Chidambaram608002 India Professor Department of Computer Applications Annamacharya Institute of Technology & Sciences YSR Kadapa Andhra Pradesh Rajampet516115 India

ISBN: (纸本)9789819768097

Colossal pattern mining, which involves deriving important relationships and insights from large and complex datasets, is an essential part of data analysis. Extracting association rules that satisfy preset minimum support and confidence criteria from a given database is the aim of association rule mining (ARM). Nevertheless, ARM often generates an unusually large number of association rules that are hard for end users to comprehend or validate, which lessens the significance of data mining discoveries. Based on the clustering methodology, this research study presents a new mining strategy. The process begins with a method called the Expressed Data Matrix (EDM), which transforms unstructured data into a structured format and enhances the quality and organisation of the information. Primary data cleaning processes are used to eliminate noise and inconsistencies from the data in order to further improve it. To address high-dimensionality issues and boost mining productivity, dimensionality reduction—which lowers the dataset to its most valuable features—is accomplished using Eigen-Principal Component Analysis (PCA). The core of this study is the hybridised clustering approach, or GS-FFO, which combines the Fruit Fly Optimisation technique (FOA) with Glow Swarm Optimisation (GSO). This technique improves the quality of clustering and reduces the probability of local optima convergence by integrating the benefits of both approaches. The suggested GS-FFO model outperforms with four datasets, including accident, pumbs*, retail, and yeast, with an accuracy of up to 98.5%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Dimensionality reduction

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Performance analysis of machine learning algorithms on automated sleep staging feature sets

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CAAI Transactions on Intelligence Technology 2021年第2期6卷 155-174页

作者： Santosh Satapathy D Loganathan Hari Kishan Kondaveeti RamaKrushna Rath Puducherry Research Scholar of Computer Science and EngineeringPondicherry Engineering CollegePuducherryIndia Professor of Computer Science and Engineering Pondicherry Engineering CollegePuducherryPuducherryIndia Assistant Professor of Computer Science and Engineering VIT UniversityAmaravatiAndhra PradeshIndia Research Scholar of Computer Science and Engineering Anna UniversityChennaiIndia

With the speeding up of social activities,rapid changes in lifestyles,and an increase in the pressure in professional fields,people are suffering from several types of sleep-related *** is a very tedious task for clinicians to monitor the entire sleep durations of the subjects and analyse the sleep staging in traditional and manual laboratory environmental *** the purpose of accurate diagnosis of different sleep disorders,we have considered the automated analysis of sleep epochs,which were collected from the subjects during sleep *** complete process of an automated approach of sleep stages5 classification is majorly executed through four steps:pre-processing the raw signals,feature extraction,feature selection,and *** this study,we have extracted 12 statistical properties from input *** proposed models are tested in three different combinations of features *** the first experiment,the feature set contained all the 12 *** second and third experiments were conducted with the nine and five best *** patient records come from the ISRUC-Sleep *** highest classification accuracy was achieved for sleep staging through combinations with the five feature *** the categories of the subjects,the reported accuracy results were found to exceed above 90%.As per the outcome from the proposed system the random forest classification techniques achieved best accuracy incomparable to that of the other two classifiers.

关键词： sleep staging diagnosis

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Survey of Various Machine Learning Techniques for Analyzing IoMT-Based Remote Patient Monitoring System 1

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International Conference on Advances in Data science and Computing Technologies, ASDC 2022

作者： Johar, Sayyed Manjula, G.R. Assistant Professor Department of Artificial Intelligence and Machine Learning Jawaharlal Nehru New College of Engineering Karnataka Shivamogga India Professor Department of Computer Science and Engineering Jawaharlal Nehru New College of Engineering Karnataka Shivamogga India

ISBN: (数字)9789819936564

ISBN: (纸本)9789819936557

Internet of Medical Things (IoMT) consists of several medical devices used to capture patient health information. The recorded details are transmitted to the healthcare centers via computer networks. This transmitted information is stored in the cloud environment for making further clinical analysis. The continuous recordings of patient information help predict the patient’s chronic disease and help to initiate the respective solutions. This recorded information is processed by applying various machine learning and deep learning techniques to predict patient health conditions. Therefore, here the general discussion of IoMT and the impact of patient health monitoring systems are discussed. Along with this, various methodology’s respective pitfalls and advantages are analyzed to improve the healthcare system’s remote patient health monitoring process. The major goals of this study were to comprehend the role played by IoMT in remote patient monitoring and the effects of intelligent approaches on data analysis. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep learning

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DoS Defense Using Modified Naive Bayes 4th

DoS Defense Using Modified Naive Bayes

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4th International Conference on Machine Intelligence and Signal Processing, MISP 2022

作者： Shrivastava, Rajesh Kumar Singh, Simar Preet Pujahari, Abinash Bennett University UP Greater Noida India Assistant Professor Computer Science and Engineering SRM University AP Andhra Pradesh Amaravati522502 India

ISBN: (纸本)9789819900848

IoT devices depend on a network for communication and convenient information sharing. However, networks suffer from security threats such as DoS attack, RPL attack. We propose a honeypot-based method to detect such attacks. In proposed solution, Cowrie honeypot is used to capture adversaries activities. Captured unlabeled data is analyses by a modified Naive Bayes algorithm in combination with K-means clustering approach. Proposed solution effectively identifies DoS attacks and helps to strengthen the firewall and intrusion detection system functionalities. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Denial-of-service attack

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Quantum-Accelerated Hyperparameter Tuning for Dynamic NLP Models

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Procedia computer science 2024年 235卷 506-519页

作者： Ravikumar S Arockia Raj Y R. Babu Vijay K R. Ramani Associate Professor Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India 600062 Assistant Professor Department of Computer Science and Engineering PSNA College of Engineering and Technology Dindigul India Assistant Professor Department of Computational Intelligence Faculty of College of Engineering and Technology SRM Institute of Science and Technology Chennai Tamilnadu India 603203 Assistant Professor Department of Computer Science and Engineering Rajalakshmi Engineering College Chennai Tamilnadu India 602117 Associate Professor Department of Computer Science and Engineering P.S.R. Engineering College Sivakasi Tamilnadu India 626140

In the rapidly evolving field of natural language processing (NLP), performance optimization of large-scale NLP models is crucial. Through the application of Quantum-Accelerated Hyperparameter Tuning (QAHT), this abstract introduces a novel approach to addressing this issue. Our proposed framework leverages quantum computing capabilities to dynamically optimize NLP model hyperparameters in real-time, catering to the ever-changing character of textual data streams. Traditional hyperparameter optimisation methods usually rely on laborious grid searches or random exploration, which may not be suitable for dynamic NLP jobs. Contrarily, QAHT uses Quantum Neural Network (QNN) architectures that have been specially designed for hyperparameter optimisation. These QNNs improve performance and efficacy by dynamically modifying and improving model configurations. This abstract discusses the key elements of the QAHT architecture, including real-time model deployment, adaptive learning, and continuous data stream processing. In addition to speeding up the hyperparameter optimisation process, QAHT makes sure that NLP models are still flexible and responsive to shifts in the sentiment of the data and its distribution. This method has applications beyond NLP since it provides a foundation for effectively optimising machine learning models in complex, real-time situations. As quantum computing develops, QAHT represents a promising future in machine learning, where quantum-enhanced capabilities satisfy the needs of contemporary data-driven applications.

关键词： Hyperparameter optimization Quantum computing Natural language processing (NLP) Quantum Neural Networks (QNN) Real-time processing Adaptive learning

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Characterization and Micro Structural Analysis of Concrete Using Industrial By-Products and Incinerated Ash

Characterization and Micro Structural Analysis of Concrete U...

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International Conference on Recent Trends in Infrastructural Development and Sustainable Materials, IC-RTIDSM 2023

作者： Dambhare, Pranita S. Dhale, S.A. Das, A. Tajne, K.M. Yadav, A. Ph.D Research Scholar Department of Electronics and Computer Science R T M Nagpur University Nagpur India Principal & Professor Priyadarshini College of Engineering Nagpur India Assistant Professor Priyadarshini College of Engineering Nagpur India Associate Professor Government College of Engineering Nagpur India Assistant Professor Yashwantrao Chavan College of Engineering Nagpur440033 India

ISBN: (纸本)9783031821325

For the last vicennial, scientists have been focusing attention on mitigating the risks of environmental contamination brought on by the manufacturing of industrial waste and cementitious materials. Finding efficient waste to effectively incorporate into concrete production is the most feasible approach. Incinerated ash, an industrial by-product, offers unique qualities and long-term advantages that make it suitable as a building material. As a result, the characteristics of waste material are thoroughly covered in this paper to assess how effective it is as filler or cement substitute in concrete. This study examines the impact of replacing silica fume in concrete with incinerated ash on the material's strength and penetration capabilities. To achieve this, four concrete mixes containing four different percentages of silica fume (6, 8, 10, and 20%) and incinerated ash (4, 6, 8, and 10%) were prepared, along with a control concrete mix. It was noted that the control-mix concrete was easier to work than the waste-mix concrete. These investigations verified that 4% incinerated ash replacement combined with 8% silica fume results in improved mechanical properties and elevated pozzolanic reactivity. In the meanwhile, further study is required, particularly in the area of durability details, to assess the comprehensive performance of concrete made from industrial and incinerated ash. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Concrete mixtures

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Heart Rate Anomaly Detection in Healthcare Using Elliptic Envelope and Local Forest

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Procedia computer science 2025年 258卷 1677-1687页

作者： Chandralekha E S. Vinodhini Kandasamy V Rama P Assistant Professor Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India-600062 Assistant Professor Department of Information Technology Velammal Engineering College Chennai Tamilnadu India-600062 Assistant Professor Panimalar Engineering College Chennai Tamilnadu India – 600123 Assistant Professor Department of Computing Technologies College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Technology Chennai Tamilnadu India-603203

Medical professionals use Anomaly Detection(AD) to identify patients with potential health problems in the heart rate data (HRD), an essential metric related to cardiovascular health. In this research, the most effective AD techniques were identified in detecting unusual variations of the heart rate pattern. Synthetic HRD data with anomalous rates of 15%, 20%, and 25% was generated and standardized it using min-max scaling for consistency among the datasets. Normal HR was based on function with a mean of 70 and a standard deviation of 5 while anomalous values were generated with a mean of 120 and a standard deviation of 10. Among the evaluated models (Elliptic Envelope, one-class SVM, Local Outlier Factor, and Isolation Forest), the Elliptic Envelope (EE) and Isolation Forest (IF) models obtain the best scores in terms of recall and precision – 0.92 and 0.89, respectively. Overall, the results of this research indicate that equally robust AD models should be implemented within clinical systems and wearable devices that serve as a digital space for early cardiac risk identification, prompting adequate intervention and, as a result, health improvement.

关键词： Anomaly Detection Heart Rate Data Isolation Forest Local Outlier Factor One-Class SVM Elliptic Envelope

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Artificial intelligence in the detection and classification of dental caries

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Journal of Prosthetic Dentistry 2025年第5期133卷 1326-1332页

作者： Ahmed, Walaa Magdy Azhari, Amr Ahmed Fawaz, Khaled Ahmed Ahmed, Hani M. Alsadah, Zainab M. Majumdar, Aritra Carvalho, Ricardo Marins Assistant Professor Department of Restorative Dentistry Faculty of Dentistry King Abdulaziz University Jeddah Saudi Arabia Associate Professor Department of Orthopedic Surgery Faculty of Medicine Cairo University Cairo Egypt Assistant Professor Department of Civil Engineering Faculty of Engineering King Abdulaziz University Jeddah Saudi Arabia Consultant in Restorative Dentistry Dental Department East Jeddah General Hospital Ministry of Health Jeddah Saudi Arabia Graduate student Department of Computer Science Computer Science and Applications Virginia Polytechnic Institute and State University Blacksburg VA United States Professor Department of Oral Biological and Medical Sciences Faculty of Dentistry University of British Columbia Vancouver BC Canada

Statement of problem: Automated detection of dental caries could enhance early detection, save clinician time, and enrich treatment decisions. However, a reliable system is lacking. Purpose: The purpose of this study was to train a deep learning model and to assess its ability to detect and classify dental caries. Material and methods: Bitewings radiographs with a 1876×1402-pixel resolution were collected, segmented, and anonymized with a radiographic image analysis software program and were identified and classified according to the modified King Abdulaziz University (KAU) classification for dental caries. The method was based on supervised learning algorithms trained on semantic segmentation tasks. Results: The mean score for the intersection-over-union of the model was 0.55 for proximal carious lesions on a 5-category segmentation assignment and a mean F1 score of 0.535 using 554 training samples. Conclusions: The study validated the high potential for developing an accurate caries detection model that will expedite caries identification, assess clinician decision-making, and improve the quality of patient care. © 2025 Editorial Council for The Journal of Prosthetic Dentistry

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